def example():
J,v = multidict({1:16, 2:19, 3:23, 4:28})
a = {(1,1):2, (1,2):3, (1,3):4, (1,4):5,
(2,1):3000, (2,2):3500, (2,3):5100, (2,4):7200,
}
I,b = multidict({1:7, 2:10000})
return I,J,v,a,b
def make_inst():
"""make_inst: prepare data for the diet model"""
F,c,d = multidict({ # cost # composition
"CQPounder": [ 360, {"Cal":556, "Carbo":39, "Protein":30,
"VitA":147,"VitC": 10, "Calc":221, "Iron":2.4}],
"Big Mac" : [ 320, {"Cal":556, "Carbo":46, "Protein":26,
"VitA":97, "VitC": 9, "Calc":142, "Iron":2.4}],
"FFilet" : [ 270, {"Cal":356, "Carbo":42, "Protein":14,
"VitA":28, "VitC": 1, "Calc": 76, "Iron":0.7}],
"Chicken" : [ 290, {"Cal":431, "Carbo":45, "Protein":20,
"VitA": 9, "VitC": 2, "Calc": 37, "Iron":0.9}],
"Fries" : [ 190, {"Cal":249, "Carbo":30, "Protein": 3,
"VitA": 0, "VitC": 5, "Calc": 7, "Iron":0.6}],
"Milk" : [ 170, {"Cal":138, "Carbo":10, "Protein": 7,
"VitA":80, "VitC": 2, "Calc":227, "Iron": 0}],
"VegJuice" : [ 100, {"Cal": 69, "Carbo":17, "Protein": 1,
"VitA":750,"VitC": 2, "Calc":18, "Iron": 0}],
})
N,a,b = multidict({ # min,max intake
"Cal" : [ 2000, 3000],
"Carbo" : [ 300, 375 ],
"Protein" : [ 50, 60 ],
"VitA" : [ 500, 750 ],
"VitC" : [ 85, 100 ],
"Calc" : [ 660, 900 ],
"Iron" : [ 6.0, 7.5 ],
})
return F, N, a, b, c, d
def make_data():
I, d = multidict({0: 80, 1: 270, 2: 250, 3: 160, 4: 180}) # demand
J, M, f = multidict({
0: [500, 1000],
1: [500, 1000],
2: [500, 1000]
}) # capacity, fixed costs
c = {
(0, 0): 4,
(0, 1): 6,
(0, 2): 9, # transportation costs
(1, 0): 5,
(1, 1): 4,
(1, 2): 7,
(2, 0): 6,
(2, 1): 3,
(2, 2): 4,
(3, 0): 8,
(3, 1): 5,
(3, 2): 3,
(4, 0): 10,
(4, 1): 8,
(4, 2): 4,
}
return I, J, d, M, f, c
def make_inst():
"""make_inst: prepare data for the diet model"""
F,c,d = multidict({ # cost # composition
"QPounder" : [ 1.84, {"Cal":510, "Carbo":34, "Protein":28,
"VitA":15, "VitC": 6, "Calc":30, "Iron":20}],
"McLean" : [ 2.19, {"Cal":370, "Carbo":35, "Protein":24, "VitA":15,
"VitC": 10, "Calc":20, "Iron":20}],
"Big Mac" : [ 1.84, {"Cal":500, "Carbo":42, "Protein":25,
"VitA": 6, "VitC": 2, "Calc":25, "Iron":20}],
"FFilet" : [ 1.44, {"Cal":370, "Carbo":38, "Protein":14,
"VitA": 2, "VitC": 0, "Calc":15, "Iron":10}],
"Chicken" : [ 2.29, {"Cal":400, "Carbo":42, "Protein":31,
"VitA": 8, "VitC": 15, "Calc":15, "Iron": 8}],
"Fries" : [ .77, {"Cal":220, "Carbo":26, "Protein": 3,
"VitA": 0, "VitC": 15, "Calc": 0, "Iron": 2}],
"McMuffin" : [ 1.29, {"Cal":345, "Carbo":27, "Protein":15,
"VitA": 4, "VitC": 0, "Calc":20, "Iron":15}],
"1% LFMilk": [ .60, {"Cal":110, "Carbo":12, "Protein": 9,
"VitA":10, "VitC": 4, "Calc":30, "Iron": 0}],
"OrgJuice" : [ .72, {"Cal": 80, "Carbo":20, "Protein": 1,
"VitA": 2, "VitC":120, "Calc": 2, "Iron": 2}],
})
N,a,b = multidict({ # min,max intake
"Cal" : [ 2000, None ],
"Carbo" : [ 350, 375 ],
"Protein" : [ 55, None ],
"VitA" : [ 100, None ],
"VitC" : [ 100, None ],
"Calc" : [ 100, None ],
"Iron" : [ 100, None ],
})
return F,N,a,b,c,d
def make_inst():
"""make_inst: prepare data for the diet model"""
F,c,d = multidict({ # cost # composition
"CQPounder": [ 360, {"Cal":556, "Carbo":39, "Protein":30,
"VitA":147,"VitC": 10, "Calc":221, "Iron":2.4}],
"Big Mac" : [ 320, {"Cal":556, "Carbo":46, "Protein":26,
"VitA":97, "VitC": 9, "Calc":142, "Iron":2.4}],
"FFilet" : [ 270, {"Cal":356, "Carbo":42, "Protein":14,
"VitA":28, "VitC": 1, "Calc": 76, "Iron":0.7}],
"Chicken" : [ 290, {"Cal":431, "Carbo":45, "Protein":20,
"VitA": 9, "VitC": 2, "Calc": 37, "Iron":0.9}],
"Fries" : [ 190, {"Cal":249, "Carbo":30, "Protein": 3,
"VitA": 0, "VitC": 5, "Calc": 7, "Iron":0.6}],
"Milk" : [ 170, {"Cal":138, "Carbo":10, "Protein": 7,
"VitA":80, "VitC": 2, "Calc":227, "Iron": 0}],
"VegJuice" : [ 100, {"Cal": 69, "Carbo":17, "Protein": 1,
"VitA":750,"VitC": 2, "Calc":18, "Iron": 0}],
})
N,a,b = multidict({ # min,max intake
"Cal" : [ 2000, 3000],
"Carbo" : [ 300, 375 ],
"Protein" : [ 50, 60 ],
"VitA" : [ 500, 750 ],
"VitC" : [ 85, 100 ],
"Calc" : [ 660, 900 ],
"Iron" : [ 6.0, 7.5 ],
})
return F,N,a,b,c,d
def make_inst():
"""make_inst: prepare data for the diet model"""
F,c,d = multidict({ # cost # composition
"QPounder" : [ 1.84, {"Cal":510, "Carbo":34, "Protein":28,
"VitA":15, "VitC": 6, "Calc":30, "Iron":20}],
"McLean" : [ 2.19, {"Cal":370, "Carbo":35, "Protein":24, "VitA":15,
"VitC": 10, "Calc":20, "Iron":20}],
"Big Mac" : [ 1.84, {"Cal":500, "Carbo":42, "Protein":25,
"VitA": 6, "VitC": 2, "Calc":25, "Iron":20}],
"FFilet" : [ 1.44, {"Cal":370, "Carbo":38, "Protein":14,
"VitA": 2, "VitC": 0, "Calc":15, "Iron":10}],
"Chicken" : [ 2.29, {"Cal":400, "Carbo":42, "Protein":31,
"VitA": 8, "VitC": 15, "Calc":15, "Iron": 8}],
"Fries" : [ .77, {"Cal":220, "Carbo":26, "Protein": 3,
"VitA": 0, "VitC": 15, "Calc": 0, "Iron": 2}],
"McMuffin" : [ 1.29, {"Cal":345, "Carbo":27, "Protein":15,
"VitA": 4, "VitC": 0, "Calc":20, "Iron":15}],
"1% LFMilk": [ .60, {"Cal":110, "Carbo":12, "Protein": 9,
"VitA":10, "VitC": 4, "Calc":30, "Iron": 0}],
"OrgJuice" : [ .72, {"Cal": 80, "Carbo":20, "Protein": 1,
"VitA": 2, "VitC":120, "Calc": 2, "Iron": 2}],
})
N,a,b = multidict({ # min,max intake
"Cal" : [ 2000, None ],
"Carbo" : [ 350, 375 ],
"Protein" : [ 55, None ],
"VitA" : [ 100, None ],
"VitC" : [ 100, None ],
"Calc" : [ 100, None ],
"Iron" : [ 100, None ],
})
return F, N, a, b, c, d
def make_data():
"""creates example data set"""
I, d = multidict({1: 80, 2: 270, 3: 250, 4: 160, 5: 180}) # demand
J, M, f = multidict({
1: [500, 1000],
2: [500, 1000],
3: [500, 1000]
}) # capacity, fixed costs
c = {
(1, 1): 4,
(1, 2): 6,
(1, 3): 9, # transportation costs
(2, 1): 5,
(2, 2): 4,
(2, 3): 7,
(3, 1): 6,
(3, 2): 3,
(3, 3): 4,
(4, 1): 8,
(4, 2): 5,
(4, 3): 3,
(5, 1): 10,
(5, 2): 8,
(5, 3): 4,
}
return I, J, d, M, f, c
def make_inst2():
I,d = multidict({1:45, 2:20, 3:30 , 4:30}) # demand
J,M = multidict({1:35, 2:50, 3:40}) # capacity
c = {(1,1):8, (1,2):9, (1,3):14 , # {(customer,factory) : cost<float>}
(2,1):6, (2,2):12, (2,3):9 ,
(3,1):10, (3,2):13, (3,3):16 ,
(4,1):9, (4,2):7, (4,3):5 ,
}
return I,J,c,d,M
def make_inst2():
I,d = multidict({1:45, 2:20, 3:30 , 4:30}) # demand
J,M = multidict({1:35, 2:50, 3:40}) # capacity
c = {(1,1):8, (1,2):9, (1,3):14 , # {(customer,factory) : cost<float>}
(2,1):6, (2,2):12, (2,3):9 ,
(3,1):10, (3,2):13, (3,3):16 ,
(4,1):9, (4,2):7, (4,3):5 ,
}
return I,J,c,d,M
def make_inst1():
I,d = multidict({1:80, 2:270, 3:250 , 4:160, 5:180}) # demand
J,M = multidict({1:500, 2:500, 3:500}) # capacity
c = {(1,1):4, (1,2):6, (1,3):9, # cost
(2,1):5, (2,2):4, (2,3):7,
(3,1):6, (3,2):3, (3,3):4,
(4,1):8, (4,2):5, (4,3):3,
(5,1):10, (5,2):8, (5,3):4,
}
return I,J,c,d,M
def make_data():
I,d = multidict({1:80, 2:270, 3:250, 4:160, 5:180}) # demand
J,M,f = multidict({1:[500,1000], 2:[500,1000], 3:[500,1000]}) # capacity, fixed costs
c = {(1,1):4, (1,2):6, (1,3):9, # transportation costs
(2,1):5, (2,2):4, (2,3):7,
(3,1):6, (3,2):3, (3,3):4,
(4,1):8, (4,2):5, (4,3):3,
(5,1):10, (5,2):8, (5,3):4,
}
return I,J,d,M,f,c
def make_data():
a = { (1,1):.25, (1,2):.15, (1,3):.2,
(2,1):.3, (2,2):.3, (2,3):.1,
(3,1):.15, (3,2):.65, (3,3):.05,
(4,1):.1, (4,2):.05, (4,3):.8
}
epsilon = 0.01
I,p = multidict({1:5, 2:6, 3:8, 4:20})
K,LB = multidict({1:.2, 2:.3, 3:.2})
return I,K,a,p,epsilon,LB
def make_data():
"""creates example data set"""
a = { (1,1):.25, (1,2):.15, (1,3):.2,
(2,1):.3, (2,2):.3, (2,3):.1,
(3,1):.15, (3,2):.65, (3,3):.05,
(4,1):.1, (4,2):.05, (4,3):.8
}
epsilon = 0.01
I,p = multidict({1:5, 2:6, 3:8, 4:20})
K,LB = multidict({1:.2, 2:.3, 3:.2})
return I,K,a,p,epsilon,LB
def make_inst1():
I,d=multidict({1:80,2:270,3:250,4:160,5:100}) # demand
J,M=multidict({1:500,2:500,3:500}) # capacity
c={
(1,1):4, (1,2):6, (1,3):9, # const
(2,1):5, (2,2):4, (2,3):7,
(3,1):6, (3,2):3, (3,3):3,
(4,1):8, (4,2):5, (4,3):3,
(5,1):10, (5,2):8, (5,3):4,
}
return I,J,c,d,M
def example():
I,d = multidict({1:80, 2:270, 3:250, 4:160, 5:180}) # demand
J,M,f = multidict({10:[500,100], 11:[500,100], 12:[500,100]}) # capacity, fixed costs
c = {(1,10):4, (1,11):6, (1,12):9, # transportation costs
(2,10):5, (2,11):4, (2,12):7,
(3,10):6, (3,11):3, (3,12):4,
(4,10):8, (4,11):5, (4,12):3,
(5,10):10, (5,11):8, (5,12):4,
}
x_pos = {1:0, 2:0, 3:0, 4:0, 5:0, 10:2, 11:2, 12:2} # positions of the points in the plane
y_pos = {1:2, 2:1, 3:0, 4:-1, 5:-2, 10:1, 11:0, 12:-1}
return I,J,d,M,f,c,x_pos,y_pos
def make_inst1():
"""creates example data set 1"""
d = {(1,1):80, (1,2):85, (1,3):300, (1,4):6, # {(customer,commodity):demand}}
(2,1):270, (2,2):160, (2,3):400, (2,4):7,
(3,1):250, (3,2):130, (3,3):350, (3,4):4,
(4,1):160, (4,2):60, (4,3):200, (4,4):3,
(5,1):180, (5,2):40, (5,3):150, (5,4):5
}
I = set([i for (i,k) in d])
K = set([k for (i,k) in d])
J,M = multidict({1:3000, 2:3000, 3:3000}) # capacity
produce = {1:[2,4], 2:[1,2,3], 3:[2,3,4]} # products that can be produced in each facility
weight = {1:5, 2:2, 3:3, 4:4} # {commodity: weight}
cost = {(1,1):4, (1,2):6, (1,3):9, # {(customer,factory): cost}
(2,1):5, (2,2):4, (2,3):7,
(3,1):6, (3,2):3, (3,3):4,
(4,1):8, (4,2):5, (4,3):3,
(5,1):10, (5,2):8, (5,3):4
}
c = {}
for i in I:
for j in J:
for k in produce[j]:
c[i,j,k] = cost[i,j] * weight[k]
return I,J,K,c,d,M
def point_set_example_a():
return multidict({
0: (0, 0),
0.33: (1.56, -0.167),
0.67: (2.22, -1),
1: (2, -2.5)
})
def make_2r():
"""creates example data set 2"""
J, p = multidict({ # jobs, processing times
1 : 2,
2 : 2,
3 : 3,
4 : 2,
5 : 5,
})
P = [(1,2), (1,3), (2,4)]
R = [1,2]
T = 6
c = {}
for j in J:
for t in range(1,T-p[j]+2):
c[j,t] = 1*(t-1+p[j])
a = {
# resource 1:
(1,1,0):2, (1,1,1):2,
(2,1,0):1, (2,1,1):1,
(3,1,0):1, (3,1,1):1, (3,1,2):1,
(4,1,0):1, (4,1,1):1,
(5,1,0):0, (5,1,1):0, (5,1,2):1, (5,1,3):0, (5,1,4):0,
# resource 2:
(1,2,0):1, (1,2,1):0,
(2,2,0):1, (2,2,1):1,
(3,2,0):0, (3,2,1):0, (3,2,2):0,
(4,2,0):1, (4,2,1):2,
(5,2,0):1, (5,2,1):2, (5,2,2):1, (5,2,3):1, (5,2,4):1,
}
RUB = {(1,1):2, (1,2):2, (1,3):2, (1,4):2, (1,5):2, (1,6):2, (1,7):2,
(2,1):2, (2,2):2, (2,3):2, (2,4):2, (2,5):2, (2,6):2, (2,7):2 }
return (J,P,R,T,p,c,a,RUB)
def make_1r():
J, p = multidict({ # jobs, processing times
1: 1,
2: 3,
3: 2,
4: 2,
})
P = [(1, 2), (1, 3), (2, 4)]
R = [1]
T = 6
c = {}
for j in J:
for t in range(1, T - p[j] + 2):
c[j, t] = 1 * (t - 1 + p[j])
a = {
(1, 1, 0): 2,
(2, 1, 0): 2,
(2, 1, 1): 1,
(2, 1, 2): 1,
(3, 1, 0): 1,
(3, 1, 1): 1,
(4, 1, 0): 1,
(4, 1, 1): 2,
}
RUB = {(1, 1): 2, (1, 2): 2, (1, 3): 1, (1, 4): 2, (1, 5): 2, (1, 6): 2}
return (J, P, R, T, p, c, a, RUB)
def make_data_trick():
T = 7 # number of periods
N = 5 # number of working periods of each staff element in a cycle
J = range(4) # shift set; 0 == rest
S = [2,3] # subset of shifts that must be kept at least consecutive days
# staff set, base cost
I,c_base = multidict({1:8000, 2:9000, 3:10000, 4:11000, 5:12000, 6:13000, 7:14000, 8:15000})
c = {}
for i in I:
for t in range(1,T+1):
for j in J:
if j == 0:
continue
c[i,t,j] = c_base[i]
if j == 3: # night shift, more expensive
c[i,t,j] *= 2
if t == T-1 or t == T: # weekend, more expensive
c[i,t,j] *= 1.5
b = {
(1,1):2, (1,2):2, (1,3):2,
(2,1):2, (2,2):2, (2,3):2,
(3,1):2, (3,2):2, (3,3):2,
(4,1):2, (4,2):2, (4,3):2,
(5,1):2, (5,2):2, (5,3):2,
(6,1):2, (6,2):2, (6,3):2,
(7,1):2, (7,2):2, (7,3):2,
}
return I,T,N,J,S,c,b
def make_inst1():
d = {(1,1):80, (1,2):85, (1,3):300, (1,4):6, # {(customer,commodity):demand}}
(2,1):270, (2,2):160, (2,3):400, (2,4):7,
(3,1):250, (3,2):130, (3,3):350, (3,4):4,
(4,1):160, (4,2):60, (4,3):200, (4,4):3,
(5,1):180, (5,2):40, (5,3):150, (5,4):5
}
I = set([i for (i,k) in d])
K = set([k for (i,k) in d])
J,M = multidict({1:3000, 2:3000, 3:3000}) # capacity
produce = {1:[2,4], 2:[1,2,3], 3:[2,3,4]} # products that can be produced in each facility
weight = {1:5, 2:2, 3:3, 4:4} # {commodity: weight}
cost = {(1,1):4, (1,2):6, (1,3):9, # {(customer,factory): cost}
(2,1):5, (2,2):4, (2,3):7,
(3,1):6, (3,2):3, (3,3):4,
(4,1):8, (4,2):5, (4,3):3,
(5,1):10, (5,2):8, (5,3):4
}
c = {}
for i in I:
for j in J:
for k in produce[j]:
c[i,j,k] = cost[i,j] * weight[k]
return I,J,K,c,d,M
def make_data_trick():
T = 7 # number of periods
N = 5 # number of working periods of each staff element in a cycle
J = range(4) # shift set; 0 == rest
S = [2,3] # subset of shifts that must be kept at least consecutive days
# staff set, base cost
I,c_base = multidict({1:8000, 2:9000, 3:10000, 4:11000, 5:12000, 6:13000, 7:14000, 8:15000})
c = {}
for i in I:
for t in range(1,T+1):
for j in J:
if j == 0:
continue
c[i,t,j] = c_base[i]
if j == 3: # night shift, more expensive
c[i,t,j] *= 2
if t == T-1 or t == T: # weekend, more expensive
c[i,t,j] *= 1.5
b = {
(1,1):2, (1,2):2, (1,3):2,
(2,1):2, (2,2):2, (2,3):2,
(3,1):2, (3,2):2, (3,3):2,
(4,1):2, (4,2):2, (4,3):2,
(5,1):2, (5,2):2, (5,3):2,
(6,1):2, (6,2):2, (6,3):2,
(7,1):2, (7,2):2, (7,3):2,
}
return I,T,N,J,S,c,b
def mk_example():
"""mk_example: book example for the single item lot sizing"""
T = 5
_,f,c,d,h = multidict({
1 : [3,1,5,1],
2 : [3,1,7,1],
3 : [3,3,3,1],
4 : [3,3,6,1],
5 : [3,3,4,1],
})
return T,f,c,d,h
def mk_example():
"""mk_example: book example for the single item lot sizing"""
T = 5
_,f,c,d,h = multidict({
1 : [3,1,5,1],
2 : [3,1,7,1],
3 : [3,3,3,1],
4 : [3,3,6,1],
5 : [3,3,4,1],
})
return T,f,c,d,h
def solve():
file_name = 'Database.xlsx'
# OGRANICZENIA
# Ilosc sztuk danego produktow K (wszystkich) w J magazynie
#J,M = multidict({'Magazyn 1': 3000, 'Magazyn 2': 3000, 'Magazyn 3':3000})
J, M = multidict(load_data(file_name, 'PojemnoscMagazynu'))
# Jakie produkty sa dostepne w J magazynie
produkt = load_data(file_name, 'DostepnoscProduktowWMagazynach')
print(produkt)
# Zapotrzebowanie J klienta na K produktu
d = load_data(file_name, 'Zapotrzebowanie')
# Lista sklepow
I = set([i for (i, k) in d])
# Lista produktow
K = set([k for (i, k) in d])
print(produkt)
# Wagi produktow
waga = load_data(file_name, 'WagaProduktu')
# Koszt transportu
koszt = load_data(file_name, 'Koszt')
# Koszt dotarcia z J magazynu do M sklepu
c = {}
for i in I:
for j in J:
for k in produkt[j]:
c[i, j, k] = koszt[i, j] * waga[
k] # Koszt*waga transportu z magazynu j do klienta i produktu k
# Model
model = mctransp(I, J, K, c, d, M)
# Optymalizacja
model.optimize()
EPS = 1.e-6
x = model.data
resultAsList = []
print("Ogolny koszt transportu wynosi:", model.getObjVal())
for i, j, k in x:
if model.getVal(x[i, j, k]) > EPS:
print("Wysylka %10g sztuk %3s z magazynu %3s do klienta %3s" %
(model.getVal(x[i, j, k]), k, j, i))
resultAsList.append([i, j, k, model.getVal(x[i, j, k])])
return model.getObjVal(), resultAsList
def example(n):
"""
Data generator for the one machine scheduling problem.
"""
J,p,r,d,w = multidict({
1:[1,4,0,3],
2:[4,0,0,1],
3:[2,2,0,2],
4:[3,4,0,3],
5:[1,1,0,1],
6:[4,5,0,2],
})
return J,p,r,d,w
def example(n):
"""
Data generator for the one machine scheduling problem.
"""
J,p,r,d,w = multidict({
1:[1,4,0,3],
2:[4,0,0,1],
3:[2,2,0,2],
4:[3,4,0,3],
5:[1,1,0,1],
6:[4,5,0,2],
})
return J,p,r,d,w
def make_data():
I, d = multidict({1:170,
2:270,
3:250,
4:190,
5:200,
6:230,
7:350,
8:450,
9:200,
10:180,
11:190
}
) # demand
J, M, f = multidict({1:[450, 1500],
2:[500, 800],
3:[500, 1200],
4:[600, 1900],
5:[400, 1000],
6:[550, 1900],
7:[350, 1800],
8:[800, 4000]
}
) # capacity, fixed costs
c = {(1, 1):5, (1, 2):6, (1, 3):9, (1, 4):4, (1, 5):6, (1, 6):4, (1, 7):9, (1, 8):5, # transportation costs
(2, 1):5, (2, 2):4, (2, 3):7, (2, 4):3, (2, 5):5, (2, 6):4, (2, 7):5, (2, 8):5,
(3, 1):6, (3, 2):3, (3, 3):4, (3, 4):3, (3, 5):2, (3, 6):9, (3, 7):6, (3, 8):4,
(4, 1):8, (4, 2):5, (4, 3):3, (4, 4):4, (4, 5):7, (4, 6):8, (4, 7):8, (4, 8):6,
(5, 1):10, (5, 2):8, (5, 3):4, (5, 4):5, (5, 5):6, (5, 6):3, (5, 7):6, (5, 8):7,
(6, 1):11, (6, 2):6, (6, 3):2, (6, 4):3, (6, 5):4, (6, 6):5, (6, 7):9, (6, 8):8,
(7, 1):11, (7, 2):6, (7, 3):2, (7, 4):3, (7, 5):5, (7, 6):5, (7, 7):2, (7, 8):4,
(8, 1):11, (8, 2):6, (8, 3):2, (8, 4):5, (8, 5):5, (8, 6):8, (8, 7):3, (8, 8):6,
(9, 1):4, (9, 2):6, (9, 3):8, (9, 4):9, (9, 5):7, (9, 6):5, (9, 7):8, (9, 8):5,
(10, 1):3, (10, 2):5, (10, 3):8, (10, 4):7, (10, 5):5, (10, 6):6, (10, 7):7, (10, 8):7,
(11, 1):6, (11, 2):9, (11, 3):8, (11, 4):3, (11, 5):2, (11, 6):6, (11, 7):6, (11, 8):8
}
return I, J, d, M, f, c
def example():
I, d = multidict({1: 80, 2: 270, 3: 250, 4: 160, 5: 180}) # demand
J, M, f = multidict({
10: [500, 100],
11: [500, 100],
12: [500, 100]
}) # capacity, fixed costs
c = {
(1, 10): 4,
(1, 11): 6,
(1, 12): 9, # transportation costs
(2, 10): 5,
(2, 11): 4,
(2, 12): 7,
(3, 10): 6,
(3, 11): 3,
(3, 12): 4,
(4, 10): 8,
(4, 11): 5,
(4, 12): 3,
(5, 10): 10,
(5, 11): 8,
(5, 12): 4,
}
x_pos = {
1: 0,
2: 0,
3: 0,
4: 0,
5: 0,
10: 2,
11: 2,
12: 2
} # positions of the points in the plane
y_pos = {1: 2, 2: 1, 3: 0, 4: -1, 5: -2, 10: 1, 11: 0, 12: -1}
return I, J, d, M, f, c, x_pos, y_pos
def eld40():
U, a, b, c, e, f, p_min, p_max = multidict({
1 : [ 94.705, 6.73, 0.00690, 100, 0.084, 36, 114],
2 : [ 94.705, 6.73, 0.00690, 100, 0.084, 36, 114],
3 : [ 309.54, 7.07, 0.02028, 100, 0.084, 60, 120],
4 : [ 369.03, 8.18, 0.00942, 150, 0.063, 80, 190],
5 : [ 148.89, 5.35, 0.01140, 120, 0.077, 47, 97],
6 : [ 222.33, 8.05, 0.01142, 100, 0.084, 68, 140],
7 : [ 287.71, 8.03, 0.00357, 200, 0.042, 110, 300],
8 : [ 391.98, 6.99, 0.00492, 200, 0.042, 135, 300],
9 : [ 455.76, 6.60, 0.00573, 200, 0.042, 135, 300],
10 : [ 722.82, 12.9, 0.00605, 200, 0.042, 130, 300],
11 : [ 635.20, 12.9, 0.00515, 200, 0.042, 94, 375],
12 : [ 654.69, 12.8, 0.00569, 200, 0.042, 94, 375],
13 : [ 913.40, 12.5, 0.00421, 300, 0.035, 125, 500],
14 : [ 1760.4, 8.84, 0.00752, 300, 0.035, 125, 500],
15 : [ 1728.3, 9.15, 0.00708, 300, 0.035, 125, 500],
16 : [ 1728.3, 9.15, 0.00708, 300, 0.035, 125, 500],
17 : [ 647.85, 7.97, 0.00313, 300, 0.035, 220, 500],
18 : [ 649.69, 7.95, 0.00313, 300, 0.035, 220, 500],
19 : [ 647.83, 7.97, 0.00313, 300, 0.035, 242, 550],
20 : [ 647.81, 7.97, 0.00313, 300, 0.035, 242, 550],
21 : [ 785.96, 6.63, 0.00298, 300, 0.035, 254, 550],
22 : [ 785.96, 6.63, 0.00298, 300, 0.035, 254, 550],
23 : [ 794.53, 6.66, 0.00284, 300, 0.035, 254, 550],
24 : [ 794.53, 6.66, 0.00284, 300, 0.035, 254, 550],
25 : [ 801.32, 7.10, 0.00277, 300, 0.035, 254, 550],
26 : [ 801.32, 7.10, 0.00277, 300, 0.035, 254, 550],
27 : [ 1055.1, 3.33, 0.52124, 120, 0.077, 10, 150],
28 : [ 1055.1, 3.33, 0.52124, 120, 0.077, 10, 150],
29 : [ 1055.1, 3.33, 0.52124, 120, 0.077, 10, 150],
30 : [ 148.89, 5.35, 0.01140, 120, 0.077, 47, 97],
31 : [ 222.92, 6.43, 0.00160, 150, 0.063, 60, 190],
32 : [ 222.92, 6.43, 0.00160, 150, 0.063, 60, 190],
33 : [ 222.92, 6.43, 0.00160, 150, 0.063, 60, 190],
34 : [ 107.87, 8.95, 0.00010, 200, 0.042, 90, 200],
35 : [ 116.58, 8.62, 0.00010, 200, 0.042, 90, 200],
36 : [ 116.58, 8.62, 0.00010, 200, 0.042, 90, 200],
37 : [ 307.45, 5.88, 0.01610, 80, 0.098, 25, 110],
38 : [ 307.45, 5.88, 0.01610, 80, 0.098, 25, 110],
39 : [ 307.45, 5.88, 0.01610, 80, 0.098, 25, 110],
40 : [ 647.83, 7.97, 0.00313, 300, 0.035, 242, 550],
})
U = list(a.keys())
return U,a,b,c,e,f,p_min,p_max,d
def eld13():
U, a, b, c, e, f, p_min, p_max = multidict({
1 : [ 550, 8.1, 0.00028, 300, 0.035, 0, 680 ],
2 : [ 309, 8.1, 0.00056, 200, 0.042, 0, 360 ],
3 : [ 307, 8.1, 0.00056, 200, 0.042, 0, 360 ],
4 : [ 240, 7.74, 0.00324, 150, 0.063, 60, 180 ],
5 : [ 240, 7.74, 0.00324, 150, 0.063, 60, 180 ],
6 : [ 240, 7.74, 0.00324, 150, 0.063, 60, 180 ],
7 : [ 240, 7.74, 0.00324, 150, 0.063, 60, 180 ],
8 : [ 240, 7.74, 0.00324, 150, 0.063, 60, 180 ],
9 : [ 240, 7.74, 0.00324, 150, 0.063, 60, 180 ],
10 : [ 126, 8.6, 0.00284, 100, 0.084, 40, 120 ],
11 : [ 126, 8.6, 0.00284, 100, 0.084, 40, 120 ],
12 : [ 126, 8.6, 0.00284, 100, 0.084, 55, 120 ],
13 : [ 126, 8.6, 0.00284, 100, 0.084, 55, 120 ],
})
return U, a, b, c, e, f, p_min, p_max
def make_data():
I = 10
d = [instance['customer_%d'%i]['demand']for i in range(1,I+1)] # demand
J,M,f = multidict({0:[8,20], 1:[20,20], 2:[20,20]}) # capacity, fixed costs
xDep = [instance['deport%d' %i]['coordinates']['x'] for i in range(len(J))]
yDep = [instance['deport%d' %i]['coordinates']['y'] for i in range(len(J))]
xCust = [instance['customer_%d' %i]['coordinates']['x'] for i in range(1,I+1)] # positions of the points in the plane
yCust = [instance['customer_%d' %i]['coordinates']['y'] for i in range(1,I+1)]
x1 = xDep + xCust
y1 = yDep + yCust
c = {}
for i in range(len(x1)):
#i = 0
for j in range(len(y1)):
#j = 1
c[i,j] = distance(x1[i],y1[i],x1[j],y1[j])
return I,J,d,M,f,c
def make_data():
# amount of customers
I = 10
# get the demand of each customer
d = [instance['customer_%d' % i]['demand'] for i in range(1, I + 1)]
# index of station, capacity, fixed costs
J, M, f = multidict({0: [8, 20], 1: [12, 20], 2: [12, 20]})
# get the x - coordinates of the depots
xDep = [instance['deport%d' %i]['coordinates']['x']\
for i in range(len(J))]
# get the y - coordinates of the depots
yDep = [instance['deport%d' %i]['coordinates']['y']\
for i in range(len(J))]
# get the x - coordinates of the customers
xCust = [instance['customer_%d' %i]['coordinates']['x']\
for i in range(1,I+1)]
# get the y - coordinates of the customers
yCust = [instance['customer_%d' %i]['coordinates']['y']\
for i in range(1,I+1)]
# concatenate the coordinates of depots and customers
x1 = xDep + xCust
y1 = yDep + yCust
# calculation of the distance between all of them -> matrix
c = {}
for i in range(len(x1)):
#i = 0
for j in range(len(y1)):
#j = 1
c[i, j] = distance(x1[i], y1[i], x1[j], y1[j])
return I, J, d, M, f, c
def make_inst2():
"""creates example data set 2"""
d = {
(1, 1): 45, # {(customer,commodity):demand}}
(2, 1): 20,
(3, 1): 30,
(4, 1): 30,
}
I = set([i for (i, k) in d])
K = set([k for (i, k) in d])
J, M = multidict({1: 35, 2: 50, 3: 40}) # {factory: capacity}}
produce = {
1: [1],
2: [1],
3: [1]
} # products that can be produced in each facility
weight = {1: 1} # {commodity: weight}
cost = {
(1, 1): 8,
(1, 2): 9,
(1, 3): 14, # {(customer,factory): cost}
(2, 1): 6,
(2, 2): 12,
(2, 3): 9,
(3, 1): 10,
(3, 2): 13,
(3, 3): 16,
(4, 1): 9,
(4, 2): 7,
(4, 3): 5,
}
c = {}
for i in I:
for j in J:
for k in produce[j]:
c[i, j, k] = cost[i, j] * weight[k]
return I, J, K, c, d, M
def make_1r():
"""creates example data set 1"""
J, p = multidict({ # jobs, processing times
1 : 1,
2 : 3,
3 : 2,
4 : 2,
})
P = [(1,2), (1,3), (2,4)]
R = [1]
T = 6
c = {}
for j in J:
for t in range(1,T-p[j]+2):
c[j,t] = 1*(t-1+p[j])
a = {
(1,1,0):2,
(2,1,0):2, (2,1,1):1, (2,1,2):1,
(3,1,0):1, (3,1,1):1,
(4,1,0):1, (4,1,1):2,
}
RUB = {(1,1):2, (1,2):2, (1,3):1, (1,4):2, (1,5):2, (1,6):2}
return (J,P,R,T,p,c,a,RUB)
def make_inst2():
d = {(1,1):45, # {(customer,commodity):demand}}
(2,1):20,
(3,1):30,
(4,1):30,
}
I = set([i for (i,k) in d])
K = set([k for (i,k) in d])
J,M = multidict({1:35, 2:50, 3:40}) # {factory: capacity}}
produce = {1:[1], 2:[1], 3:[1]} # products that can be produced in each facility
weight = {1:1} # {commodity: weight}
cost = {(1,1):8, (1,2):9, (1,3):14, # {(customer,factory): cost}
(2,1):6, (2,2):12, (2,3):9 ,
(3,1):10, (3,2):13, (3,3):16,
(4,1):9, (4,2):7, (4,3):5 ,
}
c = {}
for i in I:
for j in J:
for k in produce[j]:
c[i,j,k] = cost[i,j] * weight[k]
return I,J,K,c,d,M
minimize the total transportation cost for satisfying demand at
customers, from capacitated facilities.
Data:
I - set of customers
J - set of facilities
c[i,j] - unit transportation cost on arc (i,j)
d[i] - demand at node i
M[j] - capacity
Copyright (c) by Joao Pedro PEDROSO and Mikio KUBO, 2012
"""
from pyscipopt import Model, quicksum, multidict
I, d = multidict({1: 80, 2: 270, 3: 250, 4: 160, 5: 180}) # demand
J, M = multidict({1: 500, 2: 500, 3: 500}) # capacity
c = {
(1, 1): 4,
(1, 2): 6,
(1, 3): 9, # cost
(2, 1): 5,
(2, 2): 4,
(2, 3): 7,
(3, 1): 6,
(3, 2): 3,
(3, 3): 3,
(4, 1): 8,
(4, 2): 5,
(4, 3): 3,
(5, 1): 10,
def make_data_10():
"""
1..T: set of periods
K: set of resources
P: set of items
f[t,p]: set-up costs
g[t,p]: set-up times
c[t,p]: variable costs
d[t,p]: demand values
h[t,p]: holding costs
a[t,k,p]: amount of resource k for producing product p in period. t
M[t,k]: resource upper bounds
UB[t,p]: upper bound of production time of product p in period t
phi[(i,j)] : units of i required to produce a unit of j (j parent of i)
"""
T = 5
K = [1]
P = [1,2,3,4,5,6,7,8,9,10]
_, f, g, c, d, h, UB = multidict({
(1,1): [10, 1, 2, 0, 0.5, 24],
(1,2): [10, 1, 2, 0, 0.5, 24],
(1,3): [10, 1, 2, 0, 0.5, 24],
(1,4): [10, 1, 2, 0, 0.5, 24],
(1,5): [10, 1, 2, 0, 0.5, 24],
(1,6): [10, 1, 2, 0, 0.5, 24],
(1,7): [10, 1, 2, 0, 0.5, 24],
(1,8): [10, 1, 2, 0, 0.5, 24],
(1,9): [10, 1, 2, 0, 0.5, 24],
(1,10):[10, 1, 2, 0, 0.5, 24],
(2,1): [10, 1, 2, 0, 0.5, 24],
(2,2): [10, 1, 2, 0, 0.5, 24],
(2,3): [10, 1, 2, 0, 0.5, 24],
(2,4): [10, 1, 2, 0, 0.5, 24],
(2,5): [10, 1, 2, 0, 0.5, 24],
(2,6): [10, 1, 2, 0, 0.5, 24],
(2,7): [10, 1, 2, 0, 0.5, 24],
(2,8): [10, 1, 2, 0, 0.5, 24],
(2,9): [10, 1, 2, 0, 0.5, 24],
(2,10):[10, 1, 2, 0, 0.5, 24],
(3,1): [10, 1, 2, 0, 0.5, 24],
(3,2): [10, 1, 2, 0, 0.5, 24],
(3,3): [10, 1, 2, 0, 0.5, 24],
(3,4): [10, 1, 2, 0, 0.5, 24],
(3,5): [10, 1, 2, 0, 0.5, 24],
(3,6): [10, 1, 2, 0, 0.5, 24],
(3,7): [10, 1, 2, 0, 0.5, 24],
(3,8): [10, 1, 2, 0, 0.5, 24],
(3,9): [10, 1, 2, 0, 0.5, 24],
(3,10):[10, 1, 2, 0, 0.5, 24],
(4,1): [10, 1, 2, 0, 0.5, 24],
(4,2): [10, 1, 2, 0, 0.5, 24],
(4,3): [10, 1, 2, 0, 0.5, 24],
(4,4): [10, 1, 2, 0, 0.5, 24],
(4,5): [10, 1, 2, 0, 0.5, 24],
(4,6): [10, 1, 2, 0, 0.5, 24],
(4,7): [10, 1, 2, 0, 0.5, 24],
(4,8): [10, 1, 2, 0, 0.5, 24],
(4,9): [10, 1, 2, 0, 0.5, 24],
(4,10):[10, 1, 2, 0, 0.5, 24],
(5,1): [10, 1, 2, 0, 0.5, 24],
(5,2): [10, 1, 2, 0, 0.5, 24],
(5,3): [10, 1, 2, 0, 0.5, 24],
(5,4): [10, 1, 2, 0, 0.5, 24],
(5,5): [10, 1, 2, 0, 0.5, 24],
(5,6): [10, 1, 2, 0, 0.5, 24],
(5,7): [10, 1, 2, 0, 0.5, 24],
(5,8): [10, 1, 2, 0, 0.5, 24],
(5,9): [10, 1, 2, 0, 0.5, 24],
(5,10):[10, 1, 2, 5, 0.5, 24],
})
a = {
(1,1,1): 1, (1,1,2): 1, (1,1,3): 1, (1,1,4): 1, (1,1,5): 1, (1,1,6): 1, (1,1,7): 1, (1,1,8): 1, (1,1,9): 1, (1,1,10): 1,
(2,1,1): 1, (2,1,2): 1, (2,1,3): 1, (2,1,4): 1, (2,1,5): 1, (2,1,6): 1, (2,1,7): 1, (2,1,8): 1, (2,1,9): 1, (2,1,10): 1,
(3,1,1): 1, (3,1,2): 1, (3,1,3): 1, (3,1,4): 1, (3,1,5): 1, (3,1,6): 1, (3,1,7): 1, (3,1,8): 1, (3,1,9): 1, (3,1,10): 1,
(4,1,1): 1, (4,1,2): 1, (4,1,3): 1, (4,1,4): 1, (4,1,5): 1, (4,1,6): 1, (4,1,7): 1, (4,1,8): 1, (4,1,9): 1, (4,1,10): 1,
(5,1,1): 1, (5,1,2): 1, (5,1,3): 1, (5,1,4): 1, (5,1,5): 1, (5,1,6): 1, (5,1,7): 1, (5,1,8): 1, (5,1,9): 1, (5,1,10): 1,
}
M = {
(1,1): 25,
(2,1): 25,
(3,1): 25,
(4,1): 25,
(5,1): 25,
(6,1): 25,
(7,1): 25,
(8,1): 25,
(9,1): 25,
(10,1):25,
}
phi = { # phi[(i,j)] : units of i required to produce a unit of j (j parent of i)
(1,2):1,
(2,5):2,
(3,4):3,
(4,5):1,
(5,6):1,
(6,10):1/2.,
(3,7):1,
(7,8):3/2.,
(8,9):3,
(9,10):1
}
return T,K,P,f,g,c,d,h,a,M,UB,phi
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.11, "W05": 0.00, "W06": 0.00, "W07": 0.09}]
})
M = 20
B = 10100
FIXED_COST = 500
Scenarios = [1, 2, 3]
Parcels, Areas = multidict({
1: 13,
2: 7,
3: 13,
4: 15,
5: 8,
6: 14,
7: 8,
8: 15,
9: 14,
10: 10
})
def assignment02(SCENARIO1Revenue, SCENARIO2Revenue, SCENARIO3Revenue,
SCENARIO1Yield, SCENARIO2Yield, SCENARIO3Yield,
Varieties, WineBrand, Percentage, Costs, Areas, Parcels, BigM, Scenarios):
model = Model("assignment02")
###################
# -- Variables -- #
###################
model.addCons(rhoaux == (alpha - rho)*(1/phi_inv(beta))) #todo
model.addCons(quicksum(sigma[i]**2 * x[i] * x[i] for i in I) <= rhoaux * rhoaux)
model.setObjective(rho, "maximize")
model.data = x
return model
if __name__ == "__main__":
# portfolio
I,sigma,r = multidict(
{1:[0.07,1.01],
2:[0.09,1.05],
3:[0.1,1.08],
4:[0.2,1.10],
5:[0.3,1.20]}
)
alpha = 0.95
# beta = 0.1
for beta in [0.1, 0.05, 0.02, 0.01]:
print("\n\n\nbeta:",beta,"phi inv:",phi_inv(beta))
model = p_portfolio(I,sigma,r,alpha,beta)
model.optimize()
x = model.data
EPS = 1.e-6
print("Investment:")
print("%5s\t%8s" % ("i","x[i]"))
for i in I:
model.addCons(F[i] <= c[i]*T[i])
model.addCons(quicksum(w[i]*d[i]*T[i] for i in I) <= W)
model.setObjective(quicksum(c[i] + h[i]*d[i]*T[i]*0.5 for i in I), "minimize")
model.data = T,c
return model
if __name__ == "__main__":
# multiple item EOQ
I,F,h,d,w = multidict(
{1:[300,10,10,20],
2:[300,10,30,40],
3:[300,10,50,10]}
)
W = 2000
model = eoq_soco(I,F,h,d,w,W)
model.optimize()
T,c = model.data
EPS = 1.e-6
print("%5s\t%8s\t%8s" % ("i","T[i]","c[i]"))
for i in I:
print("%5s\t%8g\t%8g" % (i,model.getVal(T[i]),model.getVal(c[i])))
print
print("Optimal value:", model.getObjVal())
model.addCons(quicksum(a[i, k] * w_[i, k] for k in range(K)) == x[i])
model.addCons(quicksum(b[i, k] * w_[i, k] for k in range(K)) == c[i])
model.addCons(quicksum(w[i] * d[i] * x[i] for i in I) <= W)
model.setObjective(quicksum(c[i] for i in I), "minimize")
model.data = x, w
return model
if __name__ == "__main__":
# multiple item EOQ
I, F, h, d, w = multidict({
1: [300, 10, 10, 20],
2: [300, 10, 30, 40],
3: [300, 10, 50, 10]
})
W = 2000
K = 1000
a0, aK = 0.1, 10
model = eoq(I, F, h, d, w, W, a0, aK, K)
model.optimize()
x, w = model.data
EPS = 1.e-6
for v in x:
if model.getVal(x[v]) >= EPS:
print(x[v].name, "=", model.getVal(x[v]))
print("Optimal value:", model.getObjVal())
from pyscipopt import Model, quicksum, multidict
I, d = multidict({1: 80, 2: 270, 3: 250, 4: 160, 5: 180})
J, M = multidict({1: 500, 2: 500, 3: 500})
c = {
(1, 1): 4,
(1, 2): 6,
(1, 3): 9,
(2, 1): 5,
(2, 2): 4,
(2, 3): 7,
(3, 1): 6,
(3, 2): 3,
(3, 3): 3,
(4, 1): 8,
(4, 2): 5,
(4, 3): 3,
(5, 1): 10,
(5, 2): 8,
(5, 3): 4,
}
model = Model("transportation")
x = {}
for i in I:
for j in J:
x[i, j] = model.addVar(vtype="C", name="x(%s,%s)" % (i, j))
for i in I:
model.addCons(quicksum(x[i, j] for j in J if (i, j) in x) == d[i],
name="Demand(%s)" % i)
for j in J:
model.addCons(quicksum(x[i, j] for i in I if (i, j) in x) <= M[j],
name="Capacity(%s)" % j)
class SCENARIO1:
WineBrands, Revenues = multidict({
"W01": 12.39,
"W02": 11.94,
"W03": 12.15,
"W04": 13.71,
"W05": 10.88,
"W06": 12.99,
"W07": 11.92
})
Varieties, Costs, Yields, Percentages = multidict({
"Alfrocheiro": [11, {1: 1.20, 2: 1.07, 3: 0.75, 4: 1.20, 5: 0.81, 6: 0.72, 7: 0.51, 8: 1.25, 9: 1.22, 10: 1.40},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.21, "W07": 0.00}],
"Amaral": [60, {1: 1.34, 2: 1.45, 3: 1.49, 4: 0.94, 5: 0.67, 6: 1.11, 7: 0.91, 8: 1.15, 9: 0.79, 10: 0.53},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Aragonez": [42, {1: 1.01, 2: 0.90, 3: 0.52, 4: 1.32, 5: 1.15, 6: 0.58, 7: 0.80, 8: 1.15, 9: 0.76, 10: 1.40},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Aramon": [67, {1: 1.24, 2: 1.21, 3: 0.96, 4: 1.31, 5: 1.49, 6: 1.21, 7: 0.77, 8: 1.46, 9: 1.47, 10: 1.50},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.15}],
"Baga": [36, {1: 1.02, 2: 1.44, 3: 1.11, 4: 1.22, 5: 0.92, 6: 0.78, 7: 1.42, 8: 1.12, 9: 0.55, 10: 0.63},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Bastardo": [42, {1: 0.82, 2: 1.22, 3: 0.53, 4: 1.18, 5: 0.59, 6: 1.16, 7: 0.62, 8: 0.57, 9: 1.15, 10: 0.90},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Bonvedro": [52, {1: 0.64, 2: 1.30, 3: 0.71, 4: 1.11, 5: 1.05, 6: 1.42, 7: 0.85, 8: 1.03, 9: 1.09, 10: 0.55},
{"W01": 0.00, "W02": 0.00, "W03": 0.20, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Branjo": [37, {1: 1.26, 2: 1.45, 3: 1.13, 4: 1.09, 5: 0.60, 6: 0.58, 7: 1.08, 8: 0.51, 9: 0.80, 10: 1.06},
{"W01": 0.00, "W02": 0.42, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Cabinda": [62, {1: 0.75, 2: 0.83, 3: 1.15, 4: 0.73, 5: 1.33, 6: 0.69, 7: 1.10, 8: 1.15, 9: 0.84, 10: 1.30},
{"W01": 0.00, "W02": 0.00, "W03": 0.10, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Casculho": [48, {1: 0.70, 2: 1.22, 3: 1.24, 4: 0.85, 5: 0.91, 6: 1.40, 7: 1.26, 8: 1.34, 9: 0.74, 10: 0.53},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Cidadelhe": [14, {1: 1.14, 2: 0.97, 3: 1.48, 4: 0.87, 5: 1.47, 6: 1.19, 7: 1.17, 8: 1.48, 9: 1.34, 10: 0.57},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.31, "W07": 0.00}],
"Cidreiro": [21, {1: 0.64, 2: 0.73, 3: 0.98, 4: 1.44, 5: 1.39, 6: 0.56, 7: 0.79, 8: 1.46, 9: 1.44, 10: 1.26},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Concieira": [22, {1: 0.84, 2: 1.39, 3: 1.36, 4: 1.02, 5: 1.21, 6: 1.10, 7: 1.40, 8: 1.06, 9: 0.83, 10: 1.48},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Cornifesto": [66, {1: 1.48, 2: 0.90, 3: 1.09, 4: 1.01, 5: 1.43, 6: 0.98, 7: 0.55, 8: 1.40, 9: 0.64, 10: 0.81},
{"W01": 0.09, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Corropio": [24, {1: 0.94, 2: 0.84, 3: 1.21, 4: 0.53, 5: 0.59, 6: 1.48, 7: 0.76, 8: 1.25, 9: 0.70, 10: 1.06},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.10, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Corvo": [41, {1: 0.79, 2: 0.80, 3: 0.53, 4: 1.43, 5: 1.30, 6: 0.98, 7: 0.86, 8: 1.03, 9: 0.71, 10: 0.68},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Deliciosa": [85, {1: 0.94, 2: 0.86, 3: 1.33, 4: 1.40, 5: 0.65, 6: 0.59, 7: 1.08, 8: 0.74, 9: 1.21, 10: 1.13},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.08, "W06": 0.00, "W07": 0.00}],
"Donzelinho": [89, {1: 1.01, 2: 1.00, 3: 0.75, 4: 1.14, 5: 1.25, 6: 0.91, 7: 1.42, 8: 0.99, 9: 1.33, 10: 0.56},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.08, "W06": 0.00, "W07": 0.00}],
"Engomada": [74, {1: 1.42, 2: 0.51, 3: 1.07, 4: 0.92, 5: 0.93, 6: 0.87, 7: 1.25, 8: 0.94, 9: 0.60, 10: 1.25},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Espadeiro": [68, {1: 1.39, 2: 0.58, 3: 1.01, 4: 0.96, 5: 0.51, 6: 1.11, 7: 0.81, 8: 1.44, 9: 0.61, 10: 1.41},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Ferral": [60, {1: 0.76, 2: 1.03, 3: 1.39, 4: 0.68, 5: 1.18, 6: 1.26, 7: 1.13, 8: 0.55, 9: 1.13, 10: 1.11},
{"W01": 0.00, "W02": 0.00, "W03": 0.22, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Galego": [33, {1: 0.54, 2: 0.82, 3: 0.55, 4: 1.39, 5: 1.11, 6: 1.29, 7: 0.75, 8: 1.35, 9: 0.63, 10: 1.41},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.13}],
"Grangeal": [35, {1: 1.09, 2: 1.35, 3: 0.59, 4: 1.10, 5: 1.32, 6: 1.09, 7: 0.56, 8: 1.39, 9: 0.53, 10: 1.28},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.15, "W06": 0.00, "W07": 0.02}],
"Lourela": [22, {1: 1.35, 2: 1.32, 3: 0.57, 4: 1.00, 5: 0.51, 6: 1.31, 7: 0.68, 8: 1.00, 9: 0.77, 10: 0.61},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.21, "W07": 0.00}],
"Lusitano": [67, {1: 0.76, 2: 0.55, 3: 1.12, 4: 1.25, 5: 1.25, 6: 0.94, 7: 1.26, 8: 1.11, 9: 0.96, 10: 0.94},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Malandra": [84, {1: 1.09, 2: 1.49, 3: 0.98, 4: 1.16, 5: 0.74, 6: 0.95, 7: 1.32, 8: 0.72, 9: 0.57, 10: 1.24},
{"W01": 0.00, "W02": 0.05, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Malvarisco": [15, {1: 1.32, 2: 1.37, 3: 0.80, 4: 1.07, 5: 0.72, 6: 0.58, 7: 1.46, 8: 1.17, 9: 0.60, 10: 0.83},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Marufo": [96, {1: 0.74, 2: 1.33, 3: 1.32, 4: 1.20, 5: 1.43, 6: 1.48, 7: 0.97, 8: 0.56, 9: 0.69, 10: 1.43},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Melra": [60, {1: 0.81, 2: 0.89, 3: 0.63, 4: 1.33, 5: 1.35, 6: 1.01, 7: 0.51, 8: 0.99, 9: 1.15, 10: 1.01},
{"W01": 0.00, "W02": 0.00, "W03": 0.29, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.16}],
"Molar": [95, {1: 1.27, 2: 0.75, 3: 1.47, 4: 1.13, 5: 0.79, 6: 0.98, 7: 1.37, 8: 1.09, 9: 1.30, 10: 0.78},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Monvedro": [72, {1: 1.10, 2: 1.16, 3: 0.68, 4: 0.65, 5: 0.88, 6: 1.11, 7: 0.76, 8: 0.61, 9: 0.85, 10: 0.56},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Moreto": [44, {1: 1.42, 2: 1.31, 3: 1.13, 4: 1.25, 5: 1.20, 6: 0.88, 7: 1.05, 8: 0.79, 9: 1.14, 10: 0.69},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Moscatel": [11, {1: 1.47, 2: 1.40, 3: 1.22, 4: 1.38, 5: 1.17, 6: 0.97, 7: 0.80, 8: 0.57, 9: 0.66, 10: 0.67},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.08}],
"Mourisco": [24, {1: 1.49, 2: 0.89, 3: 0.78, 4: 1.02, 5: 1.07, 6: 0.60, 7: 1.08, 8: 1.18, 9: 0.76, 10: 0.98},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Mulata": [24, {1: 1.15, 2: 1.32, 3: 1.22, 4: 0.65, 5: 0.55, 6: 0.70, 7: 0.72, 8: 0.91, 9: 0.79, 10: 0.85},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Nevoeira": [67, {1: 1.12, 2: 1.25, 3: 1.02, 4: 0.70, 5: 0.76, 6: 0.80, 7: 0.73, 8: 1.17, 9: 0.66, 10: 0.62},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.22, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Padeiro": [94, {1: 1.43, 2: 0.93, 3: 1.12, 4: 1.23, 5: 0.94, 6: 0.71, 7: 1.03, 8: 0.70, 9: 0.77, 10: 1.22},
{"W01": 0.14, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.47, "W06": 0.00, "W07": 0.00}],
"Patorra": [21, {1: 1.17, 2: 0.83, 3: 0.71, 4: 1.32, 5: 0.91, 6: 0.95, 7: 1.03, 8: 0.52, 9: 1.22, 10: 1.04},
{"W01": 0.14, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.08}],
"Pedral": [79, {1: 0.87, 2: 1.21, 3: 1.37, 4: 1.03, 5: 1.48, 6: 1.07, 7: 0.52, 8: 1.12, 9: 0.92, 10: 0.68},
{"W01": 0.00, "W02": 0.06, "W03": 0.00, "W04": 0.17, "W05": 0.00, "W06": 0.00, "W07": 0.06}],
"Pilongo": [41, {1: 1.02, 2: 1.49, 3: 0.57, 4: 0.73, 5: 1.02, 6: 1.08, 7: 0.55, 8: 1.33, 9: 1.34, 10: 1.36},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Ramisco": [31, {1: 0.77, 2: 0.72, 3: 1.01, 4: 1.12, 5: 0.94, 6: 0.61, 7: 0.51, 8: 0.54, 9: 0.88, 10: 0.54},
{"W01": 0.28, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Ricoca": [86, {1: 0.59, 2: 0.79, 3: 0.68, 4: 1.37, 5: 1.35, 6: 1.50, 7: 1.42, 8: 1.02, 9: 0.71, 10: 0.87},
{"W01": 0.00, "W02": 0.00, "W03": 0.05, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Rufete": [21, {1: 0.74, 2: 1.37, 3: 0.60, 4: 1.04, 5: 1.38, 6: 0.79, 7: 1.48, 8: 1.21, 9: 1.20, 10: 1.21},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.11}],
"Saborinho": [29, {1: 1.01, 2: 1.38, 3: 1.37, 4: 1.48, 5: 1.35, 6: 1.38, 7: 0.61, 8: 0.63, 9: 1.42, 10: 0.52},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Tintinha": [12, {1: 1.45, 2: 1.48, 3: 1.45, 4: 1.46, 5: 1.10, 6: 0.95, 7: 1.38, 8: 1.30, 9: 1.27, 10: 1.07},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Touriga": [50, {1: 1.02, 2: 0.84, 3: 0.90, 4: 1.47, 5: 1.25, 6: 0.86, 7: 0.80, 8: 1.23, 9: 0.86, 10: 0.82},
{"W01": 0.23, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Trincadeira": [46, {1: 1.37, 2: 1.18, 3: 0.81, 4: 0.99, 5: 1.06, 6: 0.68, 7: 0.89, 8: 0.73, 9: 0.64, 10: 0.52},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Triunfo": [77, {1: 0.95, 2: 1.23, 3: 1.46, 4: 1.39, 5: 0.83, 6: 1.15, 7: 0.65, 8: 0.53, 9: 0.87, 10: 0.90},
{"W01": 0.00, "W02": 0.09, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Varejoa": [93, {1: 1.19, 2: 0.69, 3: 0.67, 4: 1.32, 5: 0.53, 6: 1.16, 7: 0.96, 8: 0.65, 9: 0.58, 10: 1.25},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.00, "W05": 0.00, "W06": 0.00, "W07": 0.00}],
"Xara": [10, {1: 0.55, 2: 1.40, 3: 1.00, 4: 1.04, 5: 1.29, 6: 0.70, 7: 1.29, 8: 1.13, 9: 0.94, 10: 1.38},
{"W01": 0.00, "W02": 0.00, "W03": 0.00, "W04": 0.11, "W05": 0.00, "W06": 0.00, "W07": 0.09}]
})
def mkp(I, J, v, a, b):
model = Model("mkp")
x = {}
for j in J:
x[j] = model.addVar(vtype="B", name="x[%d]" % j)
# model.update()
for i in I:
model.addCons(
quicksum(a[i, j] * x[j] for j in J) <= b[i], "Dimension[%d]" % i)
model.setObjective(quicksum(v[j] * x[j] for j in J))
# model.update()
return model
J, v = multidict({1: 16, 2: 19, 3: 23, 4: 28})
a = {
(1, 1): 2,
(1, 2): 3,
(1, 3): 4,
(1, 4): 5,
(2, 1): 3000,
(2, 2): 3500,
(2, 3): 5100,
(2, 4): 7200,
}
I, b = multidict({1: 7, 2: 10000})
model = mkp(I, J, v, a, b)
# model.ModelSense = -1
model.optimize()